Carcinogenicity Prediction of Noncongeneric Chemicals by a Support Vector Machine

Abstract

The ability to identify carcinogenic compounds is of fundamental importance to the safe application of chemicals. In this study, we generated an array of in silico models allowing the classification of compounds into carcinogenic and noncarcinogenic agents based on a data set of 852 noncongeneric chemicals collected from the Carcinogenic Potency Database (CPDBAS). Twenty-four molecular descriptors were selected by Pearson correlation, F-score, and stepwise regression analysis. These descriptors cover a range of physicochemical properties, including electrophilicity, geometry, molecular weight, size, and solubility. The descriptor mutagenic showed the highest correlation coefficient with carcinogenicity. On the basis of these descriptors, a support vector machine-based (SVM) classification model was developed and fine-tuned by a 10-fold cross-validation approach. Both the SVM model (Model A1) and the best model from the 10-fold cross-validation (Model B3) runs gave good results on the test set with prediction accuracy over 80%, sensitivity over 76%, and specificity over 82%. In addition, extended connectivity fingerprints (ECFPs) and the Toxtree software were used to analyze the functional groups and substructures linked to carcinogenicity. It was found that the results of both methods are in good agreement.